Datasets:

isp-uv-es
/

SEN2NEON

pretty_name: SEN2NEON
license: cc-by-4.0
tags:
  - remote-sensing
  - geospatial
  - sentinel-2
  - super-resolution
  - multispectral
  - earth-observation
task_categories:
  - image-to-image
task_ids:
  - super-resolution
annotations_creators:
  - no-annotation
language_creators:
  - other
language: []
multilinguality:
  - other
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    default: true
    data_files:
      - split: validation
        path: metadata.parquet

SEN2NEON

LR/HR Multispectral Super-Resolution Dataset for benchmarking and research.

Dataset Summary

SEN2NEON provides paired low-resolution (LR, 10 m) and high-resolution (HR) GeoTIFF tiles for validating super-resolution (SR) models in remote sensing. The LR product contains observed Sentinel-2 Level-2A reflectances; the HR references are derived from NEON AVIRIS-NG hyperspectral acquisitions. Each product shares the same spatial footprint and is pixel-aligned. Companion Parquet/JSONL/CSV indexes provide paths for both HR resolutions plus per-tile provenance, acquisition timing, geometry, cloud/nodata statistics, and land-cover labels for stratified evaluation (e.g., Forest vs Built-up).

The code, examples, and validation workflows can be found at ESAOpenSR/SEN2NEON.

Release correction — 20 August 2026: A previous dataset upload mistakenly provided a linearized 10 m NEON product as lr. This was corrected on 20 August 2026. The canonical LR files are now in s2_l2a_10m/ and contain the original Sentinel-2 observations exported through Google Earth Engine. The incorrect product is excluded from this release. The quantitative LR/HR consistency values reported in the paper were calculated using the real Sentinel-2 values.

HR resolution: The 2.5 m HR product is the canonical SEN2NEON reference described and evaluated in the paper. It remains the default in the metadata and companion code. We additionally provide aligned 1 m HR tiles because they are produced by our processing workflow and may be useful to others. The 1 m product is supplementary and does not redefine the published benchmark or its reported results.

Modality: 12-band multispectral GeoTIFFs
Tasks: Super-resolution (image-to-image), benchmarking, and analysis
Scale: 2,269 aligned tile IDs with one LR and two HR products
Alignment: Canonical 2.5 m HR is 4× LR; supplementary 1 m HR is 10× LR; both are integer, isotropic, and pixel-aligned
Geo: Projected UTM zones per tile; WGS84 centroids are included in the metadata
Split: One validation split; this is a benchmark, not a globally representative training corpus

Repository Layout

.
├── README.md
├── DATASET_RELEASE_NOTES.md
├── assets/
│   └── sen2neon_banner.png
├── metadata.parquet             # default HF Dataset/Viewer index
├── metadata.jsonl               # equivalent line-delimited JSON index
├── metadata.csv                 # equivalent tabular index
├── s2_l2a_10m.sha256            # SHA-256 manifest for LR tiles
├── s2_l2a_10m/                  # observed Sentinel-2 LR, 12×256×256
├── neon_2.5m_linearized/        # canonical paper HR, 12×1024×1024
└── neon_1m_linearized/          # supplementary workflow HR, 12×2560×2560

Relative paths in all metadata indexes match the on-Hub layout. The Parquet index is the default source for the Hugging Face Dataset Viewer and load_dataset; JSONL and CSV are retained as portable equivalents.

Record Schema

Each record describes one aligned tile and its LR/canonical/supplementary HR paths. Core fields are:

{
  "id": "<stem>",
  "name": "<filename.tif>",
  "split": "validation",
  "lr": "s2_l2a_10m/<file>.tif",
  "hr": "neon_2.5m_linearized/<file>.tif",
  "hr_2_5m_path": "neon_2.5m_linearized/<file>.tif",
  "hr_1m_path": "neon_1m_linearized/<file>.tif",
  "hr_available_resolutions_m": [2.5, 1.0],
  "hr_canonical_resolution_m": 2.5,
  "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
  "lon": 0.0,
  "lat": 0.0,
  "LC_detail_id": 0,
  "LC_detail_text": "<class>",
  "LC_superclass_id": 0,
  "LC_superclass_text": "<class>"
}

Additional fields record the exact Sentinel-2 and NEON asset identifiers, acquisition times, temporal separation, cloud score, CRS, reflectance scaling, and the native Sentinel-2 resolution associated with each band. Per-product hr_2_5m_* and hr_1m_* fields provide raster dimensions, pixel size, nodata value, affine transform, and canonical/supplementary status. The legacy hr and generic hr_* fields remain aliases for the canonical 2.5 m product for backward compatibility.

Notes:

split is set to "validation" for this release.
lon/lat are WGS84 centroids. The enriched index also provides the explicit aliases centroid_lon/centroid_lat.
Land-cover fields come from the categorical land-cover raster used during preprocessing.
All raster products contain reflectance stored as uint16 and scaled by 10,000.

Quick Download

Using the Hub snapshot cache (resumable, selective patterns):

pip install -U huggingface_hub
python - <<'PY'
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="isp-uv-es/SEN2NEON",
    repo_type="dataset",
    local_dir="./data/sen2neon",
    allow_patterns=[
        "README.md",
        "DATASET_RELEASE_NOTES.md",
        "metadata.*",
        "s2_l2a_10m.sha256",
        "s2_l2a_10m/**",
        "neon_2.5m_linearized/**",
    ],
)
PY

This example downloads the canonical 2.5 m product. Replace neon_2.5m_linearized/** with neon_1m_linearized/** for only the supplementary 1 m product, or include both patterns to download both.

Load with 🤗 Datasets

The default configuration loads the metadata index without downloading all imagery:

from datasets import load_dataset

ds = load_dataset("isp-uv-es/SEN2NEON", split="validation")
row = ds[0]
print(row["id"], row["lr"])
print(row["hr"])             # canonical 2.5 m alias
print(row["hr_2_5m_path"])   # explicit canonical path
print(row["hr_1m_path"])     # supplementary path

The LR and HR path columns intentionally remain relative path strings. Hugging Face's standard Image decoder relies on PIL, which cannot faithfully decode these 12-band GeoTIFFs. Download individual pairs lazily and read them with rasterio:

from huggingface_hub import hf_hub_download
import rasterio

lr_path = hf_hub_download(
    repo_id="isp-uv-es/SEN2NEON",
    filename=row["lr"],
    repo_type="dataset",
)
hr_key = "hr"  # use "hr_1m_path" for the supplementary 1 m product
hr_path = hf_hub_download(
    repo_id="isp-uv-es/SEN2NEON",
    filename=row[hr_key],
    repo_type="dataset",
)

with rasterio.open(lr_path) as src:
    lr = src.read()  # (12, 256, 256)
with rasterio.open(hr_path) as src:
    hr = src.read()  # (12, 1024, 1024) by default; 2560×2560 for 1 m

Load with PyTorch (Local Files)

After downloading into ./data/sen2neon/, use the CSV-driven loader from the companion code repository:

from data.dataset import SEN2NEON
from torch.utils.data import DataLoader

root = "./data/sen2neon"
csv_path = f"{root}/metadata.csv"

ds = SEN2NEON(
    csv_path=csv_path,
    root_dir=root,
    hr_resolution=2.5,  # canonical default; use 1 for supplementary HR
    crop_size_lr=None,
)
loader = DataLoader(ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=True)

batch = next(iter(loader))
lr, hr, meta = batch["lr"], batch["hr"], batch["meta"]
print(lr.shape, hr.shape)

Land-cover Integration

Each sample is joined with land-cover information derived from an external categorical land-cover raster covering the study area. The LR tile footprint is reprojected to the land-cover CRS; the mode value within that window is taken as the label.

LC_detail_id / LC_detail_text: fine-grained class (for example, 41 → “Deciduous”).
LC_superclass_id / LC_superclass_text: coarser super-group (for example, 40 → “Forest”).
The enriched index also provides normalized land_cover_detail* and land_cover_superclass* aliases.
These fields enable stratified metrics such as PSNR, SSIM, and SAM by environment type.
Missing values may occur outside coverage or over NoData areas.

Geospatial & Data Notes

CRS: Tiles use projected UTM zones; the exact CRS and affine transforms are included per record. Centroids are provided in WGS84.
Alignment: Canonical 2.5 m HR is an exact 4× scaling of LR; supplementary 1 m HR is an exact 10× scaling. Both are pixel-grid aligned.
Nodata: LR uses 65535 and HR uses 0 as the GeoTIFF nodata value.
Bands: All raster products contain B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, and B12. B10 is excluded.
LR grid: The LR files preserve observed Sentinel-2 radiometry, but all 12 bands are stored on one common 10 m tile grid. Bands with native 20 or 60 m resolution were sampled onto that grid by the GEE export; separate native-resolution grids are not included.
HR availability: The 2.5 m HR reference is the canonical paper product and default. The aligned 1 m workflow product is provided as a supplementary resource for other potential uses.

Intended Uses

Benchmarking SR models (classical, CNN, diffusion, and GAN approaches).
Stratified evaluation by land-cover class.
Qualitative visualization and error analysis.
Testing spectral and radiometric consistency across all released Sentinel-2 bands.
Exploring applications that benefit from the supplementary aligned 1 m workflow product.

Limitations

Land-cover labels are window-mode summaries, not per-pixel annotations.
Some tiles may lack land-cover labels or cross class boundaries.
Cross-sensor pairs may retain differences caused by temporal separation, atmosphere, illumination, sensor point-spread functions, and residual coregistration.
Coverage is limited to North American NEON sites and is imbalanced toward natural land-cover classes.
The published SEN2NEON benchmark and reported metrics use the canonical 2.5 m HR product, not the supplementary 1 m product.

Integrity

s2_l2a_10m.sha256 records the SHA-256 checksum of every canonical LR file. The corrected files were verified byte-for-byte against the archived original GEE exports before this release was staged.

License

SEN2NEON is distributed under CC BY 4.0. Please retain attribution when redistributing or deriving work from the dataset.

Citation

If you use SEN2NEON in your research, please cite:

@article{donike2026sen2neon,
  author  = {Donike, Simon and Aybar, Cesar and Contreras, Julio and G{\'o}mez-Chova, Luis},
  title   = {SEN2NEON: Enabling Quantitative Benchmarking of Sentinel-2 Superresolution for All Multispectral Bands},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume  = {23},
  pages   = {6013905--6013905},
  year    = {2026},
  doi     = {10.1109/LGRS.2026.3703947}
}

Contact

Maintainer: Image Processing Laboratory, University of Valencia, Spain
Issues and questions: open a discussion at ESAOpenSR/SEN2NEON.